'Probabilistic latent semantic analysis' presentation slideshows

LING / C SC 439/539 Statistical Natural Language Processing

977 views • 81 slides

Modeling Text and Links: Overview

Modeling Text and Links: Overview. William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University

688 views • 61 slides

Statistical Object Recognition

CS 636 Computer Vision. Statistical Object Recognition. Nathan Jacobs. Slides adapted from Lazebnik. Administrivia. Project 4 Final Project Next Class is Cancelled. Overview. Statistical Recognition generative vs. discriminative learning Bag of Features Models. Statistical Recognition.

795 views • 60 slides

Tomasz Malisiewicz tomasz@cmu Advanced Machine Perception February 2006

A Bayesian Hierarchical Model for Learning Natural Scene Categories L. Fei-Fei and P. Perona. CVPR 2005 Discovering objects and their location in images J. Sivic, B. Russell, A. Efros, A. Zisserman and B. Freeman. ICCV 2005. Tomasz Malisiewicz tomasz@cmu.edu Advanced Machine Perception

779 views • 59 slides

Machine Learning with MapReduce

Machine Learning with MapReduce. K-Means Clustering. How to MapReduce K-Means?. Given K , assign the first K random points to be the initial cluster centers Assign subsequent points to the closest cluster using the supplied distance measure

632 views • 51 slides

Statistical Debugging

618 views • 49 slides

Part 1: Bag-of-words models

710 views • 48 slides

Discriminative and generative methods for bags of features

Discriminative and generative methods for bags of features. Zebra. Non-zebra. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Image classification.

596 views • 40 slides

Mining Correlated Bursty Topic Patterns from Coordinated Text Streams

Mining Correlated Bursty Topic Patterns from Coordinated Text Streams. Xuanhui Wang, ChengXiang Zhai. Objective of this talk. Introducing a different bursty event detection model, which solves the problem from the topic perspective.

550 views • 38 slides

Extractive Spoken Document Summarization - Models and Features

Extractive Spoken Document Summarization - Models and Features. Yi-Ting Chen ( ??? ) Department of Computer Science ? Information Engineering National Taiwan Normal University, Taipei, Taiwan. 2007/02/08. Outline. Introduction Conventional Extractive Summarization Approaches

516 views • 38 slides

What is it? When would you use it? Why does it work? How do you implement it? Where does it stand in relatio

EM algorithm reading group. What is it? When would you use it? Why does it work? How do you implement it? Where does it stand in relation to other methods?. Introduction & Motivation. Theory. Practical. Comparison with other methods. Expectation Maximization (EM).

413 views • 34 slides

What is it? When would you use it? Why does it work? How do you implement it?

EM algorithm reading group. What is it? When would you use it? Why does it work? How do you implement it? Where does it stand in relation to other methods?. Introduction & Motivation. Theory. Practical. Comparison with other methods. Expectation Maximization (EM).

419 views • 34 slides

Chinese Spoken Document Retrieval and Organization

Chinese Spoken Document Retrieval and Organization. Berlin Chen ( ??? ) Associate Professor Department of Computer Science ? Information Engineering National Taiwan Normal University. 2006/08/03. Outline. Introduction Information Retrieval Models Spoken Document Summarization

471 views • 32 slides

Bag-of-features models

Bag-of-features models. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Overview: Bag-of-features models. Origins and motivation Learning visual vocabularies K-means clustering Discriminative methods Nearest-neighbor classification Distance functions

612 views • 29 slides

14.0 Linguistic Processing and Latent Topic Analysis

14.0 Linguistic Processing and Latent Topic Analysis. Latent Semantic Analysis (LSA ). T k. Topic. Words. Documents. d 1 d 2 ........ d j .......... d N. w 1 w 2 w i w M. w ij. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation.

510 views • 29 slides

Bag-of-features models

Bag-of-features models. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Overview: Bag-of-features models. Origins and motivation Image representation Feature extraction Visual vocabularies Discriminative methods Nearest-neighbor classification Distance functions

470 views • 29 slides

Bag-of-features models

446 views • 27 slides

Lecture 5: Probabilistic Latent Semantic Analysis

333 views • 23 slides

An Introduction to LDA Tools

824 views • 22 slides

Unsupervised Discovery of Fine-Grained Topic Clusters in Twitter Posts Vita G. Markman

Unsupervised Discovery of Fine-Grained Topic Clusters in Twitter Posts Vita G. Markman. 1. Introduction. Social media == new source of information and the ground for social interaction Twitter: Noisy and content-sparse data Question: Can we carve out fine grained topics within the

250 views • 13 slides